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Recent advances in LiDAR technology have opened up new possibilities forrobotic navigation. Given the widespread use of occupancy grid maps (OGMs) inrobotic motion planning, this paper aims to address the challenges ofintegrating LiDAR with OGMs. To this end, we propose ROG-Map, a uniformgrid-based OGM that maintains a local map moving along with the robot to enableefficient map operation and reduce memory costs for large-scene autonomousflight. Moreover, we present a novel incremental obstacle inflation method thatsignificantly reduces the computational cost of inflation. The proposed methodoutperforms state-of-the-art (SOTA) methods on various public datasets. Todemonstrate the effectiveness and efficiency of ROG-Map, we integrate it into acomplete quadrotor system and perform autonomous flights against both smallobstacles and large-scale scenes. During real-world flight tests with a 0.05 mresolution local map and 30mx30mx12m local map size, ROG-Map takes only 29.8%of frame time on average to update the map at a frame rate of 50 Hz (\ie, 5.96ms in 20 ms), including 0.33% (i.e., 0.66 ms) to perform obstacle inflation,demonstrating outstanding real-world performance. We release ROG-Map as anopen-source ROS package to promote the development of LiDAR-based motionplanning.

ROG-Map: An Efficient Robocentric Occupancy Grid Map for Large-scene and High-resolution LiDAR-based Motion Planning